Multiple linear regression
Whenever we have more than one input feature and want to build a linear regression model, we are in the realm of multiple linear regression. The general equation for a multiple linear regression model with k input features is:
Our assumptions about the model and about the error component ε remain the same as with simple linear regression, remembering that, as we now have more than one input feature, we assume that these are independent of each other. Instead of using simulated data to demonstrate multiple linear regression, we will analyze two real-world datasets.
Predicting CPU performance
Our first real-world dataset was presented by the researchers Dennis F. Kibler, David W. Aha, and Marc K. Albert in a 1989 paper entitled Instance-based prediction of real-valued attributes and published in the Journal of Computational Intelligence. The data contains the characteristics of different CPU models, such as the cycle time and the amount of cache memory. When...
